Motivation

Over the past six weeks, the supply chain has greatly improved, and access is no longer such a pressing issue. The issues facing the country have evolved: the issue now is acheiving heard immunity, and getting everyone vaccinated. In March, I didn’t know anyone who had gotten a vaccine. But now in April, I don’t know anyone who hasn’t gotten one. I wanted to map out where in the country people are the most hesitant to get vaccinated. Knowing the most resistant geographies could be useful information for government organizations to focus their vaccine advocacy efforts.

Anti-Vax Protesters in Texas

Mapping County-Level Vaccine Hesitancy

I investigated a CDC published dataset titled “Vaccine Hesitancy for COVID-19: County and local estimates.” The dataset was produced by the ASPE, the Office of the Assistant Secretary for Planning and Evaluation, of the U.S. Department of Health and Human Services. Specifically, the data was collected via the U.S. Census Bureau’s Household Pulse Survey.

H2: Data Wrangling

H3: Fauci with a headache, or …

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H3: Data Wrangling

{r} # my_data0 <- my_data %>% # select(!c("Percent non-Hispanic Native Hawaiian/Pacific Islander", "FIPS Code"))%>% # rename(region = State, subregion = `County Name`) %>% # mutate(region = tolower(region), subregion = tolower(subregion))%>% # rename(est_hesitant = "Estimated hesitant")%>% # rename(est_strong_hesitant = "Estimated strongly hesitant")%>% # rename(svi_cat = "SVI Category" )%>% # rename(svi = "Social Vulnerability Index (SVI)" )%>% # rename(pct_full_vaxed = "Percent adults fully vaccinated against COVID-19 as of 3/30/2021") # # my_data0$subregion <- str_remove(my_data0$subregion, " county.*") # my_data0$subregion <- str_remove(my_data0$subregion, ",.*") # # # census0 <- census%>% # mutate(subregion = gsub("\\.", "", subregion))%>% # mutate(subregion = tolower(subregion))%>% # mutate(region = gsub(".*, ", "",subregion)) %>% # mutate(subregion = gsub(", .*", "", subregion))%>% # rename(pop_est = "2019_pop_est") # census0$subregion <- str_remove(census0$subregion, " county.*") # # my_data0 <- my_data0 %>% # inner_join(census0, by =c("subregion", "region")) # head(my_data0, 100) #

{r} # usa_counties <- map_data(map = "county", region = ".") # # my_data0_map <- my_data0 %>% # inner_join(usa_counties, by =c("subregion", "region")) #

Mapping of hesitancy

{r,eval= FALSE} # ggplot(my_data0_map, aes(x = long, y = lat, group = group, fill = est_hesitant)) + # geom_polygon(color = "white", size = 0.05) + # theme_void() + # coord_fixed(ratio = 1.3) + # labs(fill = "Proportion of residents hesitant to be vaccinated") + # theme(legend.position="bottom")+ # scale_fill_distiller(palette = "Spectral") #

Mapping of strong hesitancy

{r, eval=FALSE} # ggplot(my_data0_map, aes(x = long, y = lat, group = group, fill = est_strong_hesitant)) + # geom_polygon(color = "white", size = 0.05) + # theme_void() + # coord_fixed(ratio = 1.3) + # labs(fill = "Proportion of residents STRONGLY hesitant to be vaccinated") + # theme(legend.position="bottom")+ # scale_fill_distiller(palette = "Spectral") #

{r,echo= FALSE} # ggplot(my_data0_map, aes(x = long, y = lat, group = group, fill = est_hesitant)) + # geom_polygon(color = "white", size = 0.05) + # theme_void() + # coord_fixed(ratio = 1.3) + # labs(fill = "Proportion of residents hesitant to be vaccinated") + # theme(legend.position="bottom")+ # scale_fill_distiller(palette = "Spectral") #

{r, echo= FALSE} # ggplot(my_data0_map, aes(x = long, y = lat, group = group, fill = est_strong_hesitant)) + # geom_polygon(color = "white", size = 0.05) + # theme_void() + # coord_fixed(ratio = 1.3) + # labs(fill = "Proportion of residents STRONGLY hesitant to be vaccinated") + # theme(legend.position="bottom")+ # scale_fill_distiller(palette = "Spectral") #

{r midwest} # ggplot() + # geom_polygon(data = my_data0_map, aes(x = long, y = lat, group = group, fill = est_strong_hesitant), color = "white", size = 0.05) + # geom_polygon(data = usa_states, aes(x = long, y = lat, group = group),fill="NA", colour = "black") + # theme_void() + # coord_fixed(xlim = c(-110, -90), ylim = c(42, 50), ratio = 1.3) + # labs(fill = "Proportion of residents STRONGLY hesitant to be vaccinated") + # theme(legend.position="bottom")+ # scale_fill_distiller(palette = "Spectral") # # remove legend! # guides(fill = FALSE) #

{r tenn} # ggplot() + # geom_polygon(data = my_data0_map, aes(x = long, y = lat, group = group, fill = est_hesitant), color = "white", size = 0.05) + # geom_polygon(data = usa_states, aes(x = long, y = lat, group = group),fill="NA", colour = "black") + # theme_void() + # coord_fixed(xlim = c(-95, -75), ylim = c(30, 40), ratio = 1.3) + # labs(fill = "Proportion of residents STRONGLY hesitant to be vaccinated") + # theme(legend.position="bottom")+ # scale_fill_distiller(palette = "Spectral") # # remove legend! # guides(fill = FALSE) #

H1: Analysis of Maps

I observe a striking visual pattern in the mapping of county hesitancy levels. In many places on the map, we see areas that dont

IDK yet but theres something about how state lines really impact fill. I ran a cluser analysis to see if I was making this trend up or not

Try out clustering # {r, warning=FALSE, message=FALSE} # my_data0_map_cluster <- my_data0_map %>% # select(est_hesitant, long, lat) # # # set.seed(15) # library(mclust) # county_clusts <- my_data0_map_cluster %>% # kmeans(centers = 48)%>% # fitted("classes")%>% # as.character() # # my_data0_map_cluster <- my_data0_map_cluster %>% mutate(cluster = county_clusts) # # my_data0_map_cluster %>% ggplot(aes(x = long, y = lat)) + # geom_point(aes(color = cluster), alpha = 0.5)+ # coord_fixed(ratio = 1.3) #

{r, echo=FALSE} # #usa_states <- map_data(map = "state", region = ".") # # ggplot()+ # geom_point(data = my_data0_map_cluster, aes(x = long, y = lat, color = cluster), alpha = 0.5)+ # geom_polygon(data = usa_states, aes(x = long, y = lat, group = group),fill="NA", colour = "black") + # theme_void()+ # theme(legend.position="none") #

H1: Including links and images

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